OpenRaiser/Intern-Atlas huggingface dataset:
Time Lower Bounds for the Metropolis Process and Simulated Annealing
Venue: arXiv cs.DS 2023
Year: 2023

The Metropolis process (MP) and Simulated Annealing (SA) are stochastic local search heuristics that are often used in solving combinatorial optimization problems. Despite significant interest, there are very few theoretical results regarding the quality of approximation obtained by MP and SA (with polynomially many iterations) for NP-hard optimization problems. We provide rigorous lower bounds for MP and SA with respect to the classical maximum independent set problem when the algorithms are initialized from the empty set. We establish the existence of a family of graphs for which both MP and SA fail to find approximate solutions in polynomial time. More specifically, we show that for any $\varepsilon \in (0,1)$ there are $n$-vertex graphs for which the probability SA (when limited to polynomially many iterations) will approximate the optimal solution within ratio $Ω\left(\frac{1}{n^{1-\varepsilon}}\right)$ is exponentially small. Our lower bounds extend to graphs of constant average degree $d$, illustrating the failure of MP to achieve an approximation ratio of $Ω\left(\frac{\log (d)}{d}\right)$ in polynomial time. In some cases, our impossibility results also go beyond Simulated Annealing and apply even when the temperature is chosen adaptively. Finally, we prove time lower bounds when the inputs to these algorithms are bipartite graphs, and even trees, which are known to admit polynomial-time algorithms for the independent set problem.
Fine-grained Disentangled Representation Learning for Multimodal Emotion Recognition
Venue: arXiv cs.SD 2023
Year: 2023

Multimodal emotion recognition (MMER) is an active research field that aims to accurately recognize human emotions by fusing multiple perceptual modalities. However, inherent heterogeneity across modalities introduces distribution gaps and information redundancy, posing significant challenges for MMER. In this paper, we propose a novel fine-grained disentangled representation learning (FDRL) framework to address these challenges. Specifically, we design modality-shared and modality-private encoders to project each modality into modality-shared and modality-private subspaces, respectively. In the shared subspace, we introduce a fine-grained alignment component to learn modality-shared representations, thus capturing modal consistency. Subsequently, we tailor a fine-grained disparity component to constrain the private subspaces, thereby learning modality-private representations and enhancing their diversity. Lastly, we introduce a fine-grained predictor component to ensure that the labels of the output representations from the encoders remain unchanged. Experimental results on the IEMOCAP dataset show that FDRL outperforms the state-of-the-art methods, achieving 78.34% and 79.44% on WAR and UAR, respectively.
DREAM-Talk: Diffusion-based Realistic Emotional Audio-driven Method for Single Image Talking Face Generation
Venue: arXiv cs.CV 2023
Year: 2023

The generation of emotional talking faces from a single portrait image remains a significant challenge. The simultaneous achievement of expressive emotional talking and accurate lip-sync is particularly difficult, as expressiveness is often compromised for the accuracy of lip-sync. As widely adopted by many prior works, the LSTM network often fails to capture the subtleties and variations of emotional expressions. To address these challenges, we introduce DREAM-Talk, a two-stage diffusion-based audio-driven framework, tailored for generating diverse expressions and accurate lip-sync concurrently. In the first stage, we propose EmoDiff, a novel diffusion module that generates diverse highly dynamic emotional expressions and head poses in accordance with the audio and the referenced emotion style. Given the strong correlation between lip motion and audio, we then refine the dynamics with enhanced lip-sync accuracy using audio features and emotion style. To this end, we deploy a video-to-video rendering module to transfer the expressions and lip motions from our proxy 3D avatar to an arbitrary portrait. Both quantitatively and qualitatively, DREAM-Talk outperforms state-of-the-art methods in terms of expressiveness, lip-sync accuracy and perceptual quality.
Hierarchical Optimization of Metaheuristic Algorithms and Federated Learning for Enhanced Capacity Management and Load Balancing in HetNets
Venue: arXiv cs.NI 2023
Year: 2023

This research introduces a revolutionary paradigm for HetNet management, presenting an innovative algorithmic framework that transcends traditional notions of network capacity enhancement. Our exploration delves into the intricate interplay among distinct components, weaving together metaheuristic algorithms, Neural Networks optimization, and Federated Learning approaches. The primary focus is on optimizing capacity in IoT-based heterogeneous networks while ensuring impeccable coverage and data reliability. Employing multi-layer optimization methods, we propose a dynamic model for optimal transmission strategy, strategically allocating replicas within cloud computing environments to curtail data access costs. Our algorithm not only discerns optimal data replication locations but also navigates the delicate balance between spectral efficiency and ergodic capacity in cellular IoT networks with small cells using on/off control. The orchestrated interplay between metaheuristic algorithms, Neural Networks optimization, and Federated Learning orchestrates resource reallocation, attaining an optimal balance between spectral efficiency, power utility, and ergodic capacity based on Quality of Service (QoS) requirements. Simulation results corroborate the efficacy of our approach, showcasing enhanced tradeoffs between spectral efficiency and total ergodic capacity with diminished outage probability compared to prevailing algorithms across diverse scenarios.
Compositional Zero-Shot Learning for Attribute-Based Object Reference in Human-Robot Interaction
Venue: arXiv cs.RO 2023
Year: 2023

Language-enabled robots have been widely studied over the past years to enable natural human-robot interaction and teaming in various real-world applications. Language-enabled robots must be able to comprehend referring expressions to identify a particular object from visual perception using a set of referring attributes extracted from natural language. However, visual observations of an object may not be available when it is referred to, and the number of objects and attributes may also be unbounded in open worlds. To address the challenges, we implement an attribute-based compositional zero-shot learning method that uses a list of attributes to perform referring expression comprehension in open worlds. We evaluate the approach on two datasets including the MIT-States and the Clothing 16K. The preliminary experimental results show that our implemented approach allows a robot to correctly identify the objects referred to by human commands.
Text2Analysis: A Benchmark of Table Question Answering with Advanced Data Analysis and Unclear Queries
Venue: arXiv cs.CL 2023
Year: 2023

Tabular data analysis is crucial in various fields, and large language models show promise in this area. However, current research mostly focuses on rudimentary tasks like Text2SQL and TableQA, neglecting advanced analysis like forecasting and chart generation. To address this gap, we developed the Text2Analysis benchmark, incorporating advanced analysis tasks that go beyond the SQL-compatible operations and require more in-depth analysis. We also develop five innovative and effective annotation methods, harnessing the capabilities of large language models to enhance data quality and quantity. Additionally, we include unclear queries that resemble real-world user questions to test how well models can understand and tackle such challenges. Finally, we collect 2249 query-result pairs with 347 tables. We evaluate five state-of-the-art models using three different metrics and the results show that our benchmark presents introduces considerable challenge in the field of tabular data analysis, paving the way for more advanced research opportunities.
Unexplored Frontiers: A Review of Empirical Studies of Exploratory Search
Venue: arXiv cs.IR 2023
Year: 2023

This article reviews how empirical research of exploratory search is conducted. We investigated aspects of interdisciplinarity, study settings and evaluation methodologies from a systematically selected sample of 231 publications from 2010-2021, including a total of 172 articles with empirical studies. Our results show that exploratory search is highly interdisciplinary, with the most frequently occurring publication venues including high impact venues in information science, information systems and human-computer interaction. However, taken in aggregate, the breadth of study settings investigated was limited. We found that a majority of studies (77%) focused on evaluating novel retrieval systems as opposed to investigating users' search processes. Furthermore, a disproportionate number of studies were based on scientific literature search (20.7%), a majority of which only considered searching for Computer Science articles. Study participants were generally from convenience samples, with 75% of studies composed exclusively of students and other academics. The methodologies used for evaluation were mostly quantitative, but lacked consistency between studies and validated questionnaires were rarely used. In discussion, we offer a critical analysis of our findings and suggest potential improvements for future exploratory search studies.
Infinite families $2$-designs from binary projective three-weight codes
Venue: arXiv cs.IT 2023
Year: 2023

Combinatorial designs are closely related to linear codes. In recent year, there are a lot of $t$-designs constructed from certain linear codes. In this paper, we aim to construct $2$-designs from binary three-weight codes. For any binary three-weight code $\mathcal{C}$ with length $n$, let $A_{n}(\mathcal{C})$ be the number of codewords in $\mathcal{C}$ with Hamming weight $n$, then we show that $\mathcal{C}$ holds $2$-designs when $\mathcal{C}$ is projective and $A_{n}(\mathcal{C})=1$. Furthermore, by extending some certain binary projective two-weight codes and basing on the defining set method, we construct two classes of binary projective three-weight codes which are suitable for holding $2$-designs.
Critic-Guided Decision Transformer for Offline Reinforcement Learning
Venue: arXiv cs.LG 2023
Year: 2023

Recent advancements in offline reinforcement learning (RL) have underscored the capabilities of Return-Conditioned Supervised Learning (RCSL), a paradigm that learns the action distribution based on target returns for each state in a supervised manner. However, prevailing RCSL methods largely focus on deterministic trajectory modeling, disregarding stochastic state transitions and the diversity of future trajectory distributions. A fundamental challenge arises from the inconsistency between the sampled returns within individual trajectories and the expected returns across multiple trajectories. Fortunately, value-based methods offer a solution by leveraging a value function to approximate the expected returns, thereby addressing the inconsistency effectively. Building upon these insights, we propose a novel approach, termed the Critic-Guided Decision Transformer (CGDT), which combines the predictability of long-term returns from value-based methods with the trajectory modeling capability of the Decision Transformer. By incorporating a learned value function, known as the critic, CGDT ensures a direct alignment between the specified target returns and the expected returns of actions. This integration bridges the gap between the deterministic nature of RCSL and the probabilistic characteristics of value-based methods. Empirical evaluations on stochastic environments and D4RL benchmark datasets demonstrate the superiority of CGDT over traditional RCSL methods. These results highlight the potential of CGDT to advance the state of the art in offline RL and extend the applicability of RCSL to a wide range of RL tasks.
Cross-Layer Optimization for Fault-Tolerant Deep Learning
Venue: arXiv cs.AR 2023
Year: 2023

Fault-tolerant deep learning accelerator is the basis for highly reliable deep learning processing and critical to deploy deep learning in safety-critical applications such as avionics and robotics. Since deep learning is known to be computing- and memory-intensive, traditional fault-tolerant approaches based on redundant computing will incur substantial overhead including power consumption and chip area. To this end, we propose to characterize deep learning vulnerability difference across both neurons and bits of each neuron, and leverage the vulnerability difference to enable selective protection of the deep learning processing components from the perspective of architecture layer and circuit layer respectively. At the same time, we observe the correlation between model quantization and bit protection overhead of the underlying processing elements of deep learning accelerators, and propose to reduce the bit protection overhead by adding additional quantization constrain without compromising the model accuracy. Finally, we employ Bayesian optimization strategy to co-optimize the correlated cross-layer design parameters at algorithm layer, architecture layer, and circuit layer to minimize the hardware resource consumption while fulfilling multiple user constraints including reliability, accuracy, and performance of the deep learning processing at the same time.
User-adaptive Tourist Information Dialogue System with Yes/No Classifier and Sentiment Estimator
Venue: arXiv cs.HC 2023
Year: 2023

We introduce our system developed for Dialogue Robot Competition 2023 (DRC2023). First, rule-based utterance selection and utterance generation using a large language model (LLM) are combined. We ensure the quality of system utterances while also being able to respond to unexpected user utterances. Second, dialogue flow is controlled by considering the results of the BERT-based yes/no classifier and sentiment estimator. These allow the system to adapt state transitions and sightseeing plans to the user.
Parameterized Guarantees for Almost Envy-Free Allocations
Venue: arXiv cs.GT 2023
Year: 2023

We study fair allocation of indivisible goods among agents with additive valuations. We obtain novel approximation guarantees for three of the strongest fairness notions in discrete fair division, namely envy-free up to the removal of any positively-valued good (EFx), pairwise maximin shares (PMMS), and envy-free up to the transfer of any positively-valued good (tEFx). Our approximation guarantees are in terms of an instance-dependent parameter $γ\in (0,1]$ that upper bounds, for each indivisible good in the given instance, the multiplicative range of nonzero values for the good across the agents. First, we consider allocations wherein, between any pair of agents and up to the removal of any positively-valued good, the envy is multiplicatively bounded. Specifically, the current work develops a polynomial-time algorithm that computes a $\left( \frac{2γ}{\sqrt{5+4γ}-1}\right)$-approximately EFx allocation for any given fair division instance with range parameter $γ\in (0,1]$. For instances with $γ\geq 0.511$, the obtained approximation guarantee for EFx surpasses the previously best-known approximation bound of $(ϕ-1) \approx 0.618$, here $ϕ$ denotes the golden ratio. Furthermore, for $γ\in (0,1]$, we develop a polynomial-time algorithm for finding allocations wherein the PMMS requirement is satisfied, between every pair of agents, within a multiplicative factor of $\frac{5}{6} γ$. En route to this result, we obtain novel existential and computational guarantees for $\frac{5}{6}$-approximately PMMS allocations under restricted additive valuations. Finally, we develop an algorithm that efficiently computes a $2γ$-approximately tEFx allocation. Specifically, we obtain existence and efficient computation of exact tEFx allocations for all instances with $γ\in [0.5, 1]$.
Universal Noise Annotation: Unveiling the Impact of Noisy annotation on Object Detection
Venue: arXiv cs.CV 2023
Year: 2023

For object detection task with noisy labels, it is important to consider not only categorization noise, as in image classification, but also localization noise, missing annotations, and bogus bounding boxes. However, previous studies have only addressed certain types of noise (e.g., localization or categorization). In this paper, we propose Universal-Noise Annotation (UNA), a more practical setting that encompasses all types of noise that can occur in object detection, and analyze how UNA affects the performance of the detector. We analyzed the development direction of previous works of detection algorithms and examined the factors that impact the robustness of detection model learning method. We open-source the code for injecting UNA into the dataset and all the training log and weight are also shared.
SyncDreamer for 3D Reconstruction of Endangered Animal Species with NeRF and NeuS
Venue: arXiv cs.CV 2023
Year: 2023

The main aim of this study is to demonstrate how innovative view synthesis and 3D reconstruction techniques can be used to create models of endangered species using monocular RGB images. To achieve this, we employed SyncDreamer to produce unique perspectives and NeuS and NeRF to reconstruct 3D representations. We chose four different animals, including the oriental stork, frog, dragonfly, and tiger, as our subjects for this study. Our results show that the combination of SyncDreamer, NeRF, and NeuS techniques can successfully create 3D models of endangered animals. However, we also observed that NeuS produced blurry images, while NeRF generated sharper but noisier images. This study highlights the potential of modeling endangered animals and offers a new direction for future research in this field. By showcasing the effectiveness of these advanced techniques, we hope to encourage further exploration and development of techniques for preserving and studying endangered species.
Non-Termination in Term Rewriting and Logic Programming
Venue: arXiv cs.LO 2023
Year: 2023

In this paper, we define two particular forms of non-termination, namely loops and binary chains, in an abstract framework that encompasses term rewriting and logic programming. The definition of loops relies on the notion of compatibility of binary relations. We also present a syntactic criterion for the detection of a special case of binary chains. Moreover, we describe our implementation NTI and compare its results at the Termination Competition 2023 with those of leading analyzers.
ChatGPT as a commenter to the news: can LLMs generate human-like opinions?
Venue: arXiv cs.CL 2023
Year: 2023

ChatGPT, GPT-3.5, and other large language models (LLMs) have drawn significant attention since their release, and the abilities of these models have been investigated for a wide variety of tasks. In this research we investigate to what extent GPT-3.5 can generate human-like comments on Dutch news articles. We define human likeness as `not distinguishable from human comments', approximated by the difficulty of automatic classification between human and GPT comments. We analyze human likeness across multiple prompting techniques. In particular, we utilize zero-shot, few-shot and context prompts, for two generated personas. We found that our fine-tuned BERT models can easily distinguish human-written comments from GPT-3.5 generated comments, with none of the used prompting methods performing noticeably better. We further analyzed that human comments consistently showed higher lexical diversity than GPT-generated comments. This indicates that although generative LLMs can generate fluent text, their capability to create human-like opinionated comments is still limited.
A Joint Communication and Computation Design for Semantic Wireless Communication with Probability Graph
Venue: arXiv cs.IT 2023
Year: 2023

In this paper, we delve into the challenge of optimizing joint communication and computation for semantic communication over wireless networks using a probability graph framework. In the considered model, the base station (BS) extracts the small-sized compressed semantic information through removing redundant messages based on the stored knowledge base. Specifically, the knowledge base is encapsulated in a probability graph that encapsulates statistical relations. At the user side, the compressed information is accurately deduced using the same probability graph employed by the BS. While this approach introduces an additional computational overhead for semantic information extraction, it significantly curtails communication resource consumption by transmitting concise data. We derive both communication and computation cost models based on the inference process of the probability graph. Building upon these models, we introduce a joint communication and computation resource allocation problem aimed at minimizing the overall energy consumption of the network, while accounting for latency, power, and semantic constraints. To address this problem, we obtain a closed-form solution for transmission power under a fixed semantic compression ratio. Subsequently, we propose an efficient linear search-based algorithm to attain the optimal solution for the considered problem with low computational complexity. Simulation results underscore the effectiveness of our proposed system, showcasing notable improvements compared to conventional non-semantic schemes.
Dual Attention U-Net with Feature Infusion: Pushing the Boundaries of Multiclass Defect Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

The proposed architecture, Dual Attentive U-Net with Feature Infusion (DAU-FI Net), addresses challenges in semantic segmentation, particularly on multiclass imbalanced datasets with limited samples. DAU-FI Net integrates multiscale spatial-channel attention mechanisms and feature injection to enhance precision in object localization. The core employs a multiscale depth-separable convolution block, capturing localized patterns across scales. This block is complemented by a spatial-channel squeeze and excitation (scSE) attention unit, modeling inter-dependencies between channels and spatial regions in feature maps. Additionally, additive attention gates refine segmentation by connecting encoder-decoder pathways. To augment the model, engineered features using Gabor filters for textural analysis, Sobel and Canny filters for edge detection are injected guided by semantic masks to expand the feature space strategically. Comprehensive experiments on a challenging sewer pipe and culvert defect dataset and a benchmark dataset validate DAU-FI Net's capabilities. Ablation studies highlight incremental benefits from attention blocks and feature injection. DAU-FI Net achieves state-of-the-art mean Intersection over Union (IoU) of 95.6% and 98.8% on the defect test set and benchmark respectively, surpassing prior methods by 8.9% and 12.6%, respectively. Ablation studies highlight incremental benefits from attention blocks and feature injection. The proposed architecture provides a robust solution, advancing semantic segmentation for multiclass problems with limited training data. Our sewer-culvert defects dataset, featuring pixel-level annotations, opens avenues for further research in this crucial domain. Overall, this work delivers key innovations in architecture, attention, and feature engineering to elevate semantic segmentation efficacy.